Title
Advanced Transfer Learning Approach for Improving Spanish Sentiment Analysis
Date Issued
01 January 2019
Access level
metadata only access
Resource Type
conference paper
Author(s)
Publisher(s)
Springer
Abstract
In the last years, innovative techniques like Transfer Learning have impacted strongly in Natural Language Processing, increasing massively the state-of-the-art in several challenging tasks. In particular, the Universal Language Model Fine-Tuning (ULMFiT) algorithm has proven to have an impressive performance on several English text classification tasks. In this paper, we aim at developing an algorithm for Spanish Sentiment Analysis of short texts that is comparable to the state-of-the-art. In order to do so, we have adapted the ULMFiT algorithm to this setting. Experimental results on benchmark datasets (InterTASS 2017 and InterTASS 2018) show how this simple transfer learning approach performs well when compared to fancy deep learning techniques.
Start page
112
End page
123
Volume
11835 LNAI
Language
English
OCDE Knowledge area
Bioinformática
Ciencias de la computación
Subjects
Scopus EID
2-s2.0-85075647488
Source
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Resource of which it is part
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN of the container
03029743
ISBN of the container
9783030337483
Sources of information:
Directorio de Producción Científica
Scopus